analytics

name	analytics
description	Flexible data science analytics for any dataset. Auto-discovers schema, recommends charts, exports to create-figure. Works with JSONL, JSON, CSV from any source.
allowed-tools	Bash, Read
triggers	["analyze data","analyze dataset","show insights","describe data","what's in this data","data exploration","EDA","schema discovery","chart recommendations","visualize this data"]
metadata	{"short-description":"Schema discovery + chart recommendations for any data"}
provides	["analytics"]
composes	["create-figure","task-monitor"]

Analytics Skill

Flexible data science analytics that works with any dataset. Auto-discovers schema, recommends visualizations, and exports in create-figure format.

Quick Start (Any Dataset)

cd .pi/skills/analytics

# Step 1: Discover what's in the data
./run.sh describe data.jsonl

# Step 2: See recommendations and generate chart
./run.sh chart data.jsonl --name distribution_channel -o chart.json

# Step 3: Render with create-figure
cd .agent/skills/create-figure
./run.sh metrics -i /path/to/chart.json --type bar -o chart.pdf

The Seamless Pipeline

Any Data (JSONL/JSON/CSV)
         │
         ▼
┌─────────────────────────────────┐
│     analytics describe          │  ← Discovers schema, recommends charts
│  "5 categorical, 2 numerical,   │
│   1 temporal column detected"   │
│  Recommendations:               │
│   - distribution_channel (bar)  │
│   - trend_by_date (line)        │
│   - heatmap_hour_x_day          │
└─────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────┐
│     analytics chart/group-by    │  ← Generates chart data in create-figure format
│  --name distribution_channel    │
│  -o chart.json                  │
└─────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────┐
│     create-figure metrics       │  ← Renders publication-quality PDF/PNG
│  -i chart.json --type bar       │
│  -o channel_distribution.pdf    │
└─────────────────────────────────┘

Commands

Discovery (Start Here)

Command	Description
describe <file>	Discover schema, detect column types, recommend charts

./run.sh describe sales.jsonl
# Output:
# Columns: date (temporal), product (categorical), amount (numerical), region (categorical)
# Recommendations:
#   1. distribution_product - Distribution of product
#   2. distribution_region - Distribution of region
#   3. trend_by_date - Count over date
#   4. heatmap_product_x_region - product vs region

Flexible Analysis

Command	Description
group-by <file>	Group by any column with aggregation
stats <file>	Numerical statistics and correlations
chart <file>	Generate chart spec for create-figure

# Group by any column
./run.sh group-by data.jsonl --by channel --for-figure -o by_channel.json
./run.sh group-by data.jsonl --by category --agg price --func sum

# Numerical stats
./run.sh stats data.jsonl --columns revenue,cost,profit

# Generate chart from recommendation
./run.sh chart data.jsonl --name distribution_channel -o chart.json

Timestamped Data (ingest-* outputs)

Command	Description
insights <file>	Full analysis summary (trends, sessions, patterns)
trends <file>	Viewing trends with rolling averages
sessions <file>	Session detection and binge analysis
time-patterns <file>	Hour/day distribution
evolution <file>	How preferences change over time

Output

Command	Description
export <file>	Batch export all standard charts
report <file>	Horus-style narrative report

Supported Formats

Format	Extension	Auto-Detection
JSONL	.jsonl	Line-delimited JSON
JSON	.json	Array or {data: [...]}
CSV	.csv	Comma-separated

Column Type Detection

The describe command auto-detects:

Type	Detection Logic	Recommended Charts
temporal	datetime64, date-like strings	line, area, heatmap (time axis)
numerical	int64, float64	histogram, scatter, stats
categorical	low cardinality (≤20 unique)	bar, pie, heatmap
boolean	bool dtype	pie (true/false)
text	high cardinality strings	word cloud, top-N

Chart Recommendations

Based on column types, analytics recommends:

Data Pattern	Chart Type	create-figure Command
1 categorical	bar, pie	metrics --type bar
1 temporal	line	training-curves
2 categorical	heatmap	heatmap
temporal + categorical	heatmap	heatmap
2+ numerical	correlation matrix	heatmap
1 numerical	histogram	metrics --type bar

Agent Workflow

For a project agent to analyze any dataset and visualize:

# 1. Discover schema
result = run("./run.sh describe data.jsonl --json")
recommendations = result["recommendations"]

# 2. Pick first recommendation
chart_name = recommendations[0]["name"]
cmd = recommendations[0]["create_figure_cmd"]

# 3. Generate chart data
run(f"./run.sh chart data.jsonl --name {chart_name} -o chart.json")

# 4. Render
run(f"cd .agent/skills/create-figure && ./run.sh {cmd} -i chart.json -o chart.pdf")

Examples

E-commerce Sales Data

# Data: orders.jsonl with date, product, category, amount, region

./run.sh describe orders.jsonl
# → Recommends: distribution_category, distribution_region, trend_by_date

./run.sh group-by orders.jsonl --by category --agg amount --func sum --for-figure -o revenue_by_category.json
# → {"metrics": {"Electronics": 45000, "Clothing": 32000, ...}}

cd .agent/skills/create-figure
./run.sh metrics -i revenue_by_category.json --type bar -o revenue.pdf

YouTube History (ingest-yt-history)

# Use specialized timestamped commands
./run.sh insights ~/.pi/ingest-yt-history/history.jsonl
./run.sh export ~/.pi/ingest-yt-history/history.jsonl -o ./charts --for-figure

cd .agent/skills/create-figure
./run.sh heatmap -i charts/heatmap.json -o viewing_heatmap.pdf

API Response Data

# Data: api_logs.json with endpoint, status_code, response_time, user_id

./run.sh describe api_logs.json
./run.sh stats api_logs.json --columns response_time
# → mean=245.3ms, std=89.2ms, p50=220ms, p99=450ms

./run.sh group-by api_logs.json --by endpoint --agg response_time --func mean --for-figure -o latency.json

Dependencies

# pyproject.toml
dependencies = [
    "pandas>=2.0.0",
    "typer>=0.9.0",
    "rich>=13.0.0",
]

Integration with Horus

# Horus narrative style
./run.sh insights ~/.pi/ingest-yt-history/history.jsonl --horus

# Output:
# "Your viewing patterns reveal a nocturnal tendency toward melancholic content.
#  Peak activity occurs in the twilight hours, with music consumption intensifying
#  during introspective night sessions..."